Magnetic resonance (MR) imaging (MRI) has been used for the assessment of renal perfusion. In renal perfusion MRI, the abdomen is scanned rapidly and repeatedly following a bolus injection of a contrast agent. The kinematics of the contrast agent are reflected in the intensity changes of the obtained time series of MR images. Analysis of the dynamic behavior of the signal intensity can provide valuable functional information.
Unfortunately, a perfusion MR image sequence often suffers from motion induced by breathing during acquisition. To ensure the correspondence of anatomical structures in different time frames, registration of time-series images is necessary.
The registration of time-series images is a challenging task because the appearance of the kidney changes rapidly over the course of contrast enhancement, and therefore it is not accurate to use the common approach of block matching and looking for a best match in intensities across frames. In addition, different renal tissue types do not enhance uniformly, which results in a rapidly changing image contrast.
There has been limited work on the registration of dynamic renal perfusion MR images. An image processing system was proposed to correct organ displacements using model-based segmentation. A phase difference movement detection method and a semi-automatic contour registration method were also proposed. These methods all start with a manually drawn kidney contour in one time frame. That initial contour is used to obtain a mask or a model, and then it is propagated to other images in the sequence.
Accordingly, what is desired is an integrated image registration algorithm to correct the motion induced by patient breathing for dynamic renal perfusion MR images.